Скачать или смотреть 23/6/25 | Efficient Quantum Algorithms for Learning and Simulating Many-Body Systems

Abstract: In this talk, I will present our recent results at the intersection of quantum machine learning and digital quantum simulation, with a focus on efficient learning and digital simulation protocols in many-body quantum systems. I will begin with our newly developed quantum kernel [1] and quantum active learning [2], which improve data efficiency and model generalization in supervised learning tasks. Next, I will turn to quantum simulation, where we employ digitized counterdiabatic driving to simulate the ground state of the Fermi-Hubbard model [3], reducing the adiabatic evolution time while maintaining high fidelity. I will highlight applications of these techniques to practical quantum information tasks, including quantum optimization in bin packing problems [4] and robust state transfer of edge modes in Su-Schrieffer-Heeger chains [5]. Altogether, these results showcase the potential of quantum-enhanced learning and control protocols to boost both efficiency and precision in quantum technologies.

[1] P. Rodriguez-Grasa et. al., Neural quantum kernels: training
quantum kernels with quantum neural networks,
arXiv:2401.04642, accepted by Phys. Rev. Research.
[2] Y Ding et. al., Quantum Active Learning, arXiv:2405.18230.
[3] J. Tang, et. al., Exploring ground states of Fermi-Hubbard model
on honeycomb lattices with counterdiabaticity, npj Quantum
Materials 9, 87 (2024).
[4] R Xu et. al., Digitized Counter-Diabatic Quantum Optimization
for Bin Packing Problem, arXiv:2502.15375.
[5] S. V. Romero et. al., Optimizing edge-state transfer in a Su-
Schrieffer-Heeger chain via hybrid analog-digital strategies, Phys.
Rev. Applied 21, 034033 (2024).